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M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2021-06-28 , DOI: 10.1016/j.isprsjprs.2021.06.011
Lukas Winiwarter , Katharina Anders , Bernhard Höfle

The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the minimum detectable change. To analyse geomorphic change in point cloud data, the multiscale model-to-model cloud comparison (M3C2) approach is commonly applied, which provides a statistical significance test. This test assumes planar surfaces and a uniform registration error. For natural surfaces, the planarity assumption does not necessarily apply, in which cases the value of minimal detectable change (Level of Detection) is overestimated. To overcome these limitations, we quantify an uncertainty information for each 3D point by propagating the uncertainty of the measurements themselves and of the alignment uncertainty to the 3D points. This allows the calculation of 3D covariance information for the point cloud, which we use in an extended statistical test for equality of multivariate means. Our method, called M3C2-EP, gives a less biased estimate of the Level of Detection, allowing a more appropriate significance threshold in typical cases. We verify our method in two simulated scenarios, and apply it to a time series of terrestrial laser scans of a rock glacier at two different timespans of three weeks and one year. Over the three-week period, we detect significant change at 12.5% fewer 3D locations, while quantifying additional 25.2% of change volume, when compared to the reference method of M3C2. Compared with manual assessment, M3C2-EP achieves a specificity of 0.97, where M3C2 reaches 0.86 for the one-year timespan, while sensitivity drops from 0.72 for M3C2 to 0.60 for M3C2-EP. Lower Levels of Detection enable the analysis of high-frequency monitoring data, where usually less change has occurred between successive scans, and where change is small compared to local roughness. Our method further allows the combination of data from multiple scan positions or data sources with different levels of uncertainty. The combination using error propagation ensures that every dataset is used to its full potential.



中文翻译:

M3C2-EP:通过误差传播推动 3D 地形点云变化检测的极限

地形时间序列的分析通常基于双时态变化检测和量化。对于使用激光扫描或摄影测量获得的 3D 点云,必须通过确定最小可检测变化将随机和系统噪声与表面变化信号分离。为了分析点云数据的地貌变化,通常采用多尺度模型到模型云比较(M3C2)方法,该方法提供了统计显着性检验。该测试假设为平面和统一的配准误差。对于自然表面,平面度假设不一定适用,在这种情况下,最小可检测变化(检测水平)的值被高估了。为了克服这些限制,我们通过将测量本身的不确定性和对准不确定性传播到 3D 点来量化每个 3D 点的不确定性信息。这允许计算点云的 3D 协方差信息,我们将其用于多变量均值相等性的扩展统计测试。我们的方法称为 M3C2-EP,对检测水平的估计偏差较小,在典型情况下允许更合适的显着性阈值。我们在两个模拟场景中验证了我们的方法,并将其应用于岩石冰川在三周和一年的两个不同时间跨度的时间序列陆地激光扫描。在三周内,与 M3C2 的参考方法相比,我们在 12.5% 的 3D 位置检测到显着变化,同时量化了额外 25.2% 的变化量。与人工评估相比,M3C2-EP 的特异性为 0.97,其中 M3C2 在一年时间跨度内达到 0.86,而敏感性从 M3C2 的 0.72 下降到 M3C2-EP 的 0.60。较低级别的检测能够分析高频监测数据,其中连续扫描之间通常发生的变化较小,并且与局部粗糙度相比变化较小。我们的方法进一步允许组合来自多个扫描位置或具有不同不确定性水平的数据源的数据。使用错误传播的组合确保每个数据集都被充分利用。较低级别的检测能够分析高频监测数据,其中连续扫描之间通常发生的变化较小,并且与局部粗糙度相比变化较小。我们的方法进一步允许组合来自多个扫描位置或具有不同不确定性水平的数据源的数据。使用错误传播的组合确保每个数据集都被充分利用。较低级别的检测能够分析高频监测数据,其中连续扫描之间通常发生的变化较小,并且与局部粗糙度相比变化较小。我们的方法进一步允许组合来自多个扫描位置或具有不同不确定性水平的数据源的数据。使用错误传播的组合确保每个数据集都被充分利用。

更新日期:2021-06-29
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